论文标题
在变量中误差下对离散时间ARX模型的超级抗议控制
Superstabilizing Control of Discrete-Time ARX Models under Error in Variables
论文作者
论文摘要
本文将基于多项式优化的框架应用于对噪声数据观察的外源输入（ARX）模型对自回归的超级抗议控制。记录的输入和输出值被已知界限的L-内界界噪声损坏。这是变量（EIV）中误差的实例，其中ARX系统的真实内部状态仍然未知。与嘈杂数据兼容的ARX模型的一致性集具有未知植物参数和未知噪声项之间的双线性。动态补偿器对所有一致的植物进行超级抗议的要求，使用多项式非负性约束表示，并在较大尺寸的半决赛计划的收敛层次结构中使用平方和平均值（SOS）方法求解。通过应用替代定理来消除噪声项，可以降低该方法的计算复杂性。该方法的有效性在示例ARX模型的控制中证明。
This paper applies a polynomial optimization based framework towards the superstabilizing control of an Autoregressive with Exogenous Input (ARX) model given noisy data observations. The recorded input and output values are corrupted with L-infinity bounded noise where the bounds are known. This is an instance of Error in Variables (EIV) in which true internal state of the ARX system remains unknown. The consistency set of ARX models compatible with noisy data has a bilinearity between unknown plant parameters and unknown noise terms. The requirement for a dynamic compensator to superstabilize all consistent plants is expressed using polynomial nonnegativity constraints, and solved using sum-of-squares (SOS) methods in a converging hierarchy of semidefinite programs in increasing size. The computational complexity of this method may be reduced by applying a Theorem of Alternatives to eliminate the noise terms. Effectiveness of this method is demonstrated on control of example ARX models.
